More Than Just a Search Engine – Where Elasticsearch and Solr Differ

Databases aren’t easy to manage. They occupy a lot of hardware, require valuable man-hours to maintain, and there aren’t that many people qualified to work on them if something goes very wrong. Plus, they are a mystery to most business units outside of IT. That doesn’t mean that databases aren’t an absolute necessity, though. They are. The datastore has always been a key component of any application. Now that businesses have shifted toward “big data,” people want access to even more information than ever before, which means more new databases. It’s become absolutely crucial for businesses to harness “big data” to make informed decisions about customers and their needs based on data analysis.

One of the challenges businesses face when using databases is getting the information they need out of it. All of the data in the world isn’t going to do much to help you unless you can draw insights from your data, and effectively search that data. That’s where tools like Elasticsearch and Solr come in. But which of these tools is best suited to your needs? Let’s evaluate what they’re used for and which tool matches a given use case.

It All Starts with Apache Lucene

Solr and Elasticsearch share a common heritage; Both were created to provide a high-level search engine built on Apache Lucene. Lucene is an extremely powerful search library, but is difficult to use for newcomers and doesn’t provide a stand-alone search application with REST APIs and more. Elasticsearch and Solr fill in those gaps and provide a whole lot more. Both offer an effective way to retrieve the information you need from your database, without having to understand all of the ins and outs of Lucene itself. Most of the work is done for you, so searching for information becomes really simple.

Essentially, these tools take index data as it’s placed inside them, making it easier to retrieve or reference that information. Both Solr and Elasticsearch are popular tools with large and active communities (which means there’s plenty of help to be had on sites like Stack Overflow in the event something is amiss). The most common use for Solr and Elasticsearch is for enterprise search and providing search, wherever it’s needed, like search on a company’s website.

The Elasticsearch Advantage

If all you’re looking for is a search function you can integrate into your website, then either tool will do. The difference is only evident when you try to scale or do more with the tool you’ve chosen.

One of the initial advantages of Elasticsearch is the built in clustering. Elasticsearch makes the creation, scaling and management of the cluster a lot easier, because it’s all neatly rolled into Elasticsearch. This has been a focus of Elasticsearch from the beginning, so Elasticsearch clusters provide a lot more capabilities to manage and scale the cluster. On the other hand, Solr requires external tools like Zookeeper to manage the cluster and didn’t really offer the scalability of Elasticsearch until SolrCloud was launched and it just hasn’t caught up.

When you look at the functionality of both products, Solr’s features don’t really go far beyond the search function—that’s its primary focus. So if you’re hoping to do something with the data you pull into a search, Solr isn’t the tool you want.

Elasticsearch is another story. Though the underlying technology of both tools is essentially the same, it’s the layer working on top of that foundation that separates them. Elasticsearch has been seeking to do more with their tool for some time now. Their search function is still extremely feature rich, but in the past few years, they’ve put more focus on expanding the use cases for the data.. An entire ecosystem has formed around Elasticsearch that provides these additional capabilities, called the Elastic Stack. The Elastic Stack includes tools for grabbing data (Beats), processing/transforming data (Logstash), visualizing data (Kibana), and more. In other words, Elasticsearch can provide functionality that’s normally reserved for expensive business intelligence suites.

In the end, Elasticsearch took the costs involved in developing a tool in-house, or paying for a third-party Business Intelligence (BI) tool, and threw it aside. For anyone in need of BI, and looking to save tens of thousands, it’s a great option.

A Comprehensive Database Solution

Now, the one remaining problem with Elasticsearch is that, under normal circumstances, you still have to implement and maintain the tool. For those who would rather have someone else deal with the hassle of hosting, maintaining, and optimizing the database (and, by extension, Elasticsearch), you can use a database solution like ObjectRocket.

At ObjectRocket, we maintain all the hardware, pay all the energy bills, and keep the software up to date. You don’t have to handle migrations, cloud storage, or even security concerns (we provide our own security solution in-house, so you don’t have to deal with more expensive options like XPack). So if you’d like to see how well your database performs when it’s built on MongoDB and utilizes Elasticsearch for search and analytics, sign up and start your 30-day free trial.

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